Urban rooftop photovoltaic (RPV) systems are crucial for energy transition in the built environment. Although artificial intelligence (AI) has been widely adopted in this domain, existing studies remain methodologically fragmented and lack a workflow-oriented comparative synthesis. This study conducts a scoping review to systematically examine the methodological development and workflow evolution of AI-based urban RPV potential assessment. A total of 524 articles were initially retrieved from Web of Science and Scopus. In total, 48 peer-reviewed studies were selected through a structured screening process. The results reveal a clear transition from conventional machine learning toward deep learning, multimodal learning, and increasingly integrated hybrid workflows. Geometry-based, parameter-based, end-to-end estimation, and hybrid workflows were identified as the dominant workflow paradigms, reflecting different balances between automation, scalability, interpretability, and physical realism. The review further highlights challenges related to transferability, benchmarking heterogeneity, uncertainty propagation, and data dependency under heterogeneous urban conditions. Overall, this study provides a workflow-oriented synthesis and comparative analytical framework of AI-based urban RPV potential assessment through a workflow taxonomy perspective highlights future directions toward more generalizable, physically informed, and adaptive urban energy modelling frameworks for solar-integrated urban planning and built-environment decarbonization, and intelligent urban energy system development across heterogeneous urban contexts.
Tian et al. (Mon,) studied this question.